
The application of SLAM technology in indoor navigation to complex indoor environments
- 1 Nanjing Technology University
* Author to whom correspondence should be addressed.
Abstract
This paper reviews the application progress of Simultaneous localization and mapping (SLAM) in complex indoor environments. SLAM is a technology used to manufacture mobile robots and autonomous vehicle. It can achieve autonomous localization and mapping in unknown environments. In dealing with complex indoor environments, SLAM technology faces many challenges, such as missing local perspectives, detecting moving objects, constructing dense maps, and real-time requirements in large-scale environments. However, with the development of technology, more and more SLAM technologies are being applied to complex indoor environments, among which the fusion of multimodal vision and deep learning technology, enhanced camera positioning technology, and navigation algorithms based on intelligent platforms are currently relatively advanced technologies. This article will briefly introduce the main development process of SLAM technology in dealing with complex indoor environments and the application of deep learning technology. By utilizing this technology, map features can be extracted more accurately, objects can be recognized, and obstacle avoidance can be achieved. This provides a good development direction for SLAM technology.
Keywords
simultaneous localization and mapping, SLAM technology, indoor environment, multi-sensor fusion, deep learning.
[1]. S. Wei, F. Pang, Z. Liu et al. ‘A Review of Simultaneous Localization and Mapping Method Based on Laser Radar’, Computer application research, vol. 2020, pp. 327-332.
[2]. Esidine Ebel. ‘Research Based on Laser Radar and Monocular Camera Heterogeneous Sensor Data Fusion Technology [D]’. Tianjin university, vol. 2021, pp. 1-75.
[3]. C. Guan. ‘Built Figure Positioning and Navigation Based on Multi-sensor Fusion [D]’, Harbin Institute of Technology, vol. 2021. pp. 1-81
[4]. S. Deng, C. Guo. ‘Research on Real-time Location and Map Construction Method Based on Multi-sensor Fusion [C]’, Academic Exchange Center of China Satellite Navigation System Administration Office. Proceedings of the 11th Annual China Satellite Navigation Conference -- S13 Autonomous Navigation. vol. 2020:6. pp. 1-6.
[5]. X. Ma et al., ‘DIR-SLAM: Dynamic Interference Removal for Real-Time VSLAM in Dynamic Environments’, Mobile Information Systems, vol. 2023, pp. 1-12, Feb. 2023, doi: 10.1155/2023/1145346.
[6]. P. Li, L. Yin, J. Gao, and Y. Sun, ‘Semantic Optimization of Feature-Based SLAM’, Mathematical Problems in Engineering, vol. 2021, pp. 1-10, Apr. 2021, doi: 10.1155/2021/5581788.
[7]. X. Zhao, T. Zuo, and X. Hu, ‘OFM-SLAM: A Visual Semantic SLAM for Dynamic Indoor Environments’, Mathematical Problems in Engineering, vol. 2021, pp. 1-16, Apr. 2021, doi: 10.1155/2021/5538840.
[8]. X. Zhu, F. Deng, Y. Ou, L. Liu, and E. Wang, ‘Vision-Based Semantic Unscented Fast SLAM for Indoor Service Robot’, Mathematical Problems in Engineering, vol. 2015, pp. 1-10, 2015, doi: 10.1155/2015/149206.
[9]. L. Wang et al., ‘Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots’, Complexity, vol. 2018, pp. 1-12, 2018, doi: 10.1155/2018/1627185.
[10]. Y. You, P. Wei, J. Cai, W. Huang, R. Kang, and H. Liu, ‘MISD-SLAM: Multimodal Semantic SLAM for Dynamic Environments’, Wireless Communications and Mobile Computing, vol. 2022, pp. 1-13, Apr. 2022, doi: 10.1155/2022/7600669.
Cite this article
Hu,Y. (2023). The application of SLAM technology in indoor navigation to complex indoor environments. Applied and Computational Engineering,12,52-57.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Mechatronics and Smart Systems
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